Small worlds and board interlocking in Brazil: a longitudinal study of corporate networks, 1997-2007

Social Network Analysis (SNA) is an emerging research field in finance, above all in Brazil. This work is pioneering in that it is supported by reference to different areas of knowledge: social network analysis and corporate governance, for dealing with a similarly emerging topic in finance; interlocking boards, the purpose being to check the validity of the small-world model in the Brazilian capital market, and the existence of associations between the positioning of the firm in the network of corporate relationships and its worth. To do so official data relating to more than 400 companies listed in Brazil between 1997 and 2007 were used. The main results obtained suggest that the configuration of the networks of relationships between board members and companies reflects the small-world model. Furthermore, there seems to be a significant relationship between the firm’s centrality and its worth, described according to an “inverted U” curve, which suggests the existence of optimum values of social prominence in the corporate network.Board Interlocking; Social Network Analysis in Finance; Company Boards

An exploratory social network analysis of academic research networks

For several decades, academics around the world have been collaborating with the view to support the development of their research domain. Having said that, the majority of scientific and technological policies try to encourage the creation of strong inter-related research groups in order to improve the efficiency of research outcomes and subsequently research funding allocation. In this paper, we attempt to highlight and thus, to demonstrate how these collaborative networks are developing in practice. To achieve this, we have developed an automated tool for extracting data about joint article publications and analyzing them from the perspective of social network analysis. In this case study, we have limited data from works published in 2010 by England academic and research institutions. The outcomes of this work can help policy makers in realising the current status of research collaborative networks in England

Firm dynamic governance of global innovation by means of flexible networks of connections

Today a plethora of inter-company alliances exists. Firms have networked value chains, disclosing consequently their strategy, which assets are internalized or externalized, and their ability to cope with fast change. The picture of all interfirm alliances in high tech sectors is that of an unstable complex network, or macrostructure, that evolves quickly and into which firms are differently entwined. Structural metrics borrowed from network research in sociology such as centrality and constraint (or lack of “structural holes”) can be used to assess dynamically a firm’s position in the macro structure and therefore the market: does the firm occupy a dominant or dominated position in an industry? How do its partners and competitors perform? Drawing also from recent theories on complex networks developed by statistical physicists, we show that firms are embedded in dynamic complex networks that have a ‘scale-free’ format, with only a few firms or “hubs” controlling the system, as well as a cohesive or ‘small-world’ structure. This small-world structure, which allows rapid diffusion of innovation along very short paths, also constrains firms continuously and can lead to a fast reversal of their position on the market. Taking as an example a major sector of the biopharmaceutical industry, this study offers insights for managers to assess effectively their environment and navigate under constant pressure within these ever-changing networks.Innovation; alliances; structural holes; centrality; complex networks; small world

Structural patterns in complex networks through spectral analysis

The study of some structural properties of networks is introduced from a graph spectral perspective. First, subgraph centrality of nodes is defined and used to classify essential proteins in a proteomic map. This index is then used to produce a method that allows the identification of superhomogeneous networks. At the same time this method classify non-homogeneous network into three universal classes of structure. We give examples of these classes from networks in different real-world scenarios. Finally, a communicability function is studied and showed as an alternative for defining communities in complex networks. Using this approach a community is unambiguously defined and an algorithm for its identification is proposed and exemplified in a real-world network

Accounting for the Role of Long Walks on Networks via a New Matrix Function

We introduce a new matrix function for studying graphs and real-world networks based on a double-factorial penalization of walks between nodes in a graph. This new matrix function is based on the matrix error function. We find a very good approximation of this function using a matrix hyperbolic tangent function. We derive a communicability function, a subgraph centrality and a double-factorial Estrada index based on this new matrix function. We obtain upper and lower bounds for the double-factorial Estrada index of graphs, showing that they are similar to those of the single-factorial Estrada index. We then compare these indices with the single-factorial one for simple graphs and real-world networks. We conclude that for networks containing chordless cycles---holes---the two penalization schemes produce significantly different results. In particular, we study two series of real-world networks representing urban street networks, and protein residue networks. We observe that the subgraph centrality based on both indices produce significantly different ranking of the nodes. The use of the double factorial penalization of walks opens new possibilities for studying important structural properties of real-world networks where long-walks play a fundamental role, such as the cases of networks containing chordless cycles

Distinctiveness Centrality in Social Networks

The determination of node centrality is a fundamental topic in social network studies. As an addition to established metrics, which identify central nodes based on their brokerage power, the number and weight of their connections, and the ability to quickly reach all other nodes, we introduce five new measures of Distinctiveness Centrality. These new metrics attribute a higher score to nodes keeping a connection with the network periphery. They penalize links to highly-connected nodes and serve the identification of social actors with more distinctive network ties. We discuss some possible applications and properties of these newly introduced metrics, such as their upper and lower bounds. Distinctiveness centrality provides a viewpoint of centrality alternative to that of established metrics

Critical Nodes In Directed Networks

Critical nodes or "middlemen" have an essential place in both social and economic networks when considering the flow of information and trade. This paper extends the concept of critical nodes to directed networks. We identify strong and weak middlemen. Node contestability is introduced as a form of competition in networks; a duality between uncontested intermediaries and middlemen is established. The brokerage power of middlemen is formally expressed and a general algorithm is constructed to measure the brokerage power of each node from the networks adjacency matrix. Augmentations of the brokerage power measure are discussed to encapsulate relevant centrality measures. We use these concepts to identify and measure middlemen in two empirical socio-economic networks, the elite marriage network of Renaissance Florence and Krackhardt's advice network.Comment: 28 pages, 6 figures, 2 table